import json
import copy
import time
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
from matplotlib import pyplot as plt
from torchsummary import summary
from nmfd_gnn import NMFD_GNN
print (torch.cuda.is_available())
device = torch.device("cuda:0")
random_seed = 42
random.seed(random_seed)
torch.manual_seed(random_seed)
torch.cuda.manual_seed(random_seed)
r = random.random
True
#1.1: settings
M = 20 #number of time interval in a window
missing_ratio = 0.50
file_name = "m_" + str(M) + "_missing_" + str(int(missing_ratio*100))
print (file_name)
#1.2: hyperparameters
num_epochs, batch_size, learning_rate = 200, 16, 0.001
beta_flow, beta_occ, beta_phy = 1.0, 1.0, 0.1
batch_size_vt = 16 #batch size for evaluation and test
delta_ratio = 0.1 #the ratio of delta in the standard deviation of flow
hyper = {"n_e": num_epochs, "b_s": batch_size, "b_s_vt": batch_size_vt, "l_r": learning_rate,\
"beta_f": beta_flow, "beta_o": beta_occ, "beta_p": beta_phy, "delta_ratio": delta_ratio}
gnn_dim_1, gnn_dim_2, gnn_dim_3, lstm_dim = 2, 128, 128, 128
p_dim = 10 #column dimension of L1, L2
c_k = 5.5 #meter, the sum of loop width and uniform vehicle length. based on Gero and Daganzo 2008.
theta_ini = [-2.879, 5.207, -2.473, 1.722, 3.619]
hyper_model = {"g_dim_1": gnn_dim_1, "g_dim_2": gnn_dim_2, "g_dim_3": gnn_dim_3, "l_dim": lstm_dim,\
"p_dim": p_dim, "c_k": c_k, "theta_ini": theta_ini}
max_no_decrease = 30
#1.3: set paths
root_path = "/home/umni2/a/umnilab/users/xue120/umni4/2023_mfd_traffic_london/"
file_path = root_path + "2_prepare_data/" + file_name + "/"
train_path, vali_path, test_path =\
file_path + "train.json", file_path + "vali.json", file_path + "test.json"
sensor_id_path = file_path + "sensor_id_order.json"
sensor_adj_path = file_path + "sensor_adj.json"
mean_std_path = file_path + "mean_std.json"
m_20_missing_50
def visualize_train_loss(total_phy_flow_occ_loss):
plt.figure(figsize=(4,3), dpi=75)
t_p_f_o_l = np.array(total_phy_flow_occ_loss)
e_loss, p_loss, f_loss, o_loss = t_p_f_o_l[:,0], t_p_f_o_l[:,1], t_p_f_o_l[:,2], t_p_f_o_l[:,3]
x = range(len(e_loss))
plt.plot(x, p_loss, linewidth=1, label = "phy loss")
plt.plot(x, f_loss, linewidth=1, label = "flow loss")
plt.plot(x, o_loss, linewidth=1, label = "occ loss")
plt.legend()
plt.title('Loss decline on train')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.savefig(file_name + '/' + 'train_loss.png', bbox_inches = 'tight')
plt.show()
def visualize_flow_loss(vali_f_mae, test_f_mae):
plt.figure(figsize=(4,3), dpi=75)
x = range(len(vali_f_mae))
plt.plot(x, vali_f_mae, linewidth=1, label="Validate")
plt.plot(x, test_f_mae, linewidth=1, label="Test")
plt.legend()
plt.title('MAE of flow on validate/test')
plt.xlabel('Epoch')
plt.ylabel('MAE (veh/h)')
plt.savefig(file_name + '/' + 'flow_mae.png', bbox_inches = 'tight')
plt.show()
def visualize_occ_loss(vali_o_mae, test_o_mae):
plt.figure(figsize=(4,3), dpi=75)
x = range(len(vali_o_mae))
plt.plot(x, vali_o_mae, linewidth=1, label="Validate")
plt.plot(x, test_o_mae, linewidth=1, label="Test")
plt.legend()
plt.title('MAE of occupancy on validate/test')
plt.xlabel('Epoch')
plt.ylabel('MAE')
plt.savefig(file_name + '/' + 'occ_mae.png',bbox_inches = 'tight')
plt.show()
def MAELoss(yhat, y):
return float(torch.mean(torch.div(torch.abs(yhat-y), 1)))
def RMSELoss(yhat, y):
return float(torch.sqrt(torch.mean((yhat-y)**2)))
def vali_test(model, f, f_mask, o, o_mask, f_o_mean_std, b_s_vt):
flow_std, occ_std, n = f_o_mean_std[1], f_o_mean_std[3], len(f)
f_mae_list, f_rmse_list, o_mae_list, o_rmse_list, num_list = list(), list(), list(), list(), list()
for i in range(0, n, b_s_vt):
s, e = i, np.min([i+b_s_vt, n])
num_list.append(e-s)
bf, bo, bf_mask, bo_mask = f[s: e], o[s: e], f_mask[s: e], o_mask[s: e]
bf_hat, bo_hat, bq_hat, bq_theta = model.run(bf_mask, bo_mask)
bf_hat, bo_hat = bf_hat.cpu(), bo_hat.cpu()
bf_mae, bf_rmse = MAELoss(bf_hat, bf)*flow_std, RMSELoss(bf_hat, bf)*flow_std
bo_mae, bo_rmse = MAELoss(bo_hat, bo)*occ_std, RMSELoss(bo_hat, bo)*occ_std
f_mae_list.append(bf_mae)
f_rmse_list.append(bf_rmse)
o_mae_list.append(bo_mae)
o_rmse_list.append(bo_rmse)
f_mae, o_mae = np.dot(f_mae_list, num_list)/n, np.dot(o_mae_list, num_list)/n
f_rmse = np.sqrt(np.dot(np.multiply(f_rmse_list, f_rmse_list), num_list)/n)
o_rmse = np.sqrt(np.dot(np.multiply(o_rmse_list, o_rmse_list), num_list)/n)
return f_mae, f_rmse, o_mae, o_rmse
def evaluate(model, vt_f, vt_o, vt_f_m, vt_o_m, f_o_mean_std, b_s_vt): #vt: vali_test
vt_f_mae, vt_f_rmse, vt_o_mae, vt_o_rmse =\
vali_test(model, vt_f, vt_f_m, vt_o, vt_o_m, f_o_mean_std, b_s_vt)
return vt_f_mae, vt_f_rmse, vt_o_mae, vt_o_rmse
import torch
#4.1: one training epoch
def train_epoch(model, opt, criterion, train_f_x, train_f_y, train_o_x, train_o_y, hyper, flow_std_squ, delta):
#f: flow; o: occupancy
model.train()
losses, p_losses, f_losses, o_losses = list(), list(), list(), list()
beta_f, beta_o, beta_p, b_s = hyper["beta_f"], hyper["beta_o"], hyper["beta_p"], hyper["b_s"]
n = len(train_f_x)
print ("# batch: ", int(n/b_s))
for i in range(0, n-b_s, b_s):
time1 = time.time()
x_f_batch, y_f_batch = train_f_x[i: i+b_s], train_f_y[i: i+b_s]
x_o_batch, y_o_batch = train_o_x[i: i+b_s], train_o_y[i: i+b_s]
opt.zero_grad()
y_f_hat, y_o_hat, q_hat, q_theta = model.run(x_f_batch, x_o_batch)
#p_loss = criterion(q_hat, q_theta).cpu() #physical loss
#p_loss = p_loss/flow_std_squ
#hinge loss
q_gap = q_hat - q_theta
delta_gap = torch.ones(q_gap.shape, device=device)*delta
zero_gap = torch.zeros(q_gap.shape, device=device) #(n, m)
hl_loss = torch.max(q_gap-delta_gap, zero_gap) + torch.max(-delta_gap-q_gap, zero_gap)
hl_loss = hl_loss/flow_std_squ
p_loss = criterion(hl_loss, zero_gap).cpu() #(n, m)
f_loss = criterion(y_f_hat.cpu(), y_f_batch) #data loss of flow
o_loss = criterion(y_o_hat.cpu(), y_o_batch) #data loss of occupancy
loss = beta_f*f_loss + beta_o*o_loss + beta_p*p_loss
loss.backward()
opt.step()
losses.append(loss.data.numpy())
p_losses.append(p_loss.data.numpy())
f_losses.append(f_loss.data.numpy())
o_losses.append(o_loss.data.numpy())
if i % (64*b_s) == 0:
print ("i_batch: ", i/b_s)
print ("the loss for this batch: ", loss.data.numpy())
print ("flow loss", f_loss.data.numpy())
print ("occ loss", o_loss.data.numpy())
time2 = time.time()
print ("time for this batch", time2-time1)
print ("----------------------------------")
n_loss = float(len(losses)+0.000001)
aver_loss = sum(losses)/n_loss
aver_p_loss = sum(p_losses)/n_loss
aver_f_loss = sum(f_losses)/n_loss
aver_o_loss = sum(o_losses)/n_loss
return aver_loss, model, aver_p_loss, aver_f_loss, aver_o_loss
#4.2: all train epochs
def train_process(model, criterion, train, vali, test, hyper, f_o_mean_std):
total_phy_flow_occ_loss = list()
n_mse_flow_occ = 0 #mse(flow) + mse(occ) for validation sets.
f_std = f_o_mean_std[1]
vali_f, vali_o = vali["flow"], vali["occupancy"]
vali_f_m, vali_o_m = vali["flow_mask"].to(device), vali["occupancy_mask"].to(device)
test_f, test_o = test["flow"], test["occupancy"]
test_f_m, test_o_m = test["flow_mask"].to(device), test["occupancy_mask"].to(device)
l_r, n_e = hyper["l_r"], hyper["n_e"]
opt = optim.Adam(model.parameters(), l_r, betas = (0.9,0.999), weight_decay=0.0001)
opt_scheduler = torch.optim.lr_scheduler.MultiStepLR(opt, milestones=[150])
print ("# epochs ", n_e)
r_vali_f_mae, r_vali_o_mae, r_test_f_mae, r_test_o_mae = list(), list(), list(), list()
r_vali_f_rmse, r_vali_o_rmse, r_test_f_rmse, r_test_o_rmse = list(), list(), list(), list()
flow_std_squ = np.power(f_std, 2)
no_decrease = 0
for i in range(n_e):
print ("----------------an epoch starts-------------------")
#time1_s = time.time()
time_s = time.time()
print ("i_epoch: ", i)
n_train = len(train["flow"])
number_list = copy.copy(list(range(n_train)))
random.shuffle(number_list, random = r)
shuffle_idx = torch.tensor(number_list)
train_x_f, train_y_f = train["flow_mask"][shuffle_idx], train["flow"][shuffle_idx]
train_x_o, train_y_o = train["occupancy_mask"][shuffle_idx], train["occupancy"][shuffle_idx]
delta = hyper["delta_ratio"] * f_std
aver_loss, model, aver_p_loss, aver_f_loss, aver_o_loss =\
train_epoch(model, opt, criterion, train_x_f.to(device), train_y_f,\
train_x_o.to(device), train_y_o, hyper, flow_std_squ, delta)
opt_scheduler.step()
total_phy_flow_occ_loss.append([aver_loss, aver_p_loss, aver_f_loss, aver_o_loss])
print ("train loss for this epoch: ", round(aver_loss, 6))
#evaluate
b_s_vt = hyper["b_s_vt"]
vali_f_mae, vali_f_rmse, vali_o_mae, vali_o_rmse =\
evaluate(model, vali_f, vali_o, vali_f_m, vali_o_m, f_o_mean_std, b_s_vt)
test_f_mae, test_f_rmse, test_o_mae, test_o_rmse =\
evaluate(model, test_f, test_o, test_f_m, test_o_m, f_o_mean_std, b_s_vt)
r_vali_f_mae.append(vali_f_mae)
r_test_f_mae.append(test_f_mae)
r_vali_o_mae.append(vali_o_mae)
r_test_o_mae.append(test_o_mae)
r_vali_f_rmse.append(vali_f_rmse)
r_test_f_rmse.append(test_f_rmse)
r_vali_o_rmse.append(vali_o_rmse)
r_test_o_rmse.append(test_o_rmse)
visualize_train_loss(total_phy_flow_occ_loss)
visualize_flow_loss(r_vali_f_mae, r_test_f_mae)
visualize_occ_loss(r_vali_o_mae, r_test_o_mae)
time_e = time.time()
print ("time for this epoch", time_e - time_s)
performance = {"train": total_phy_flow_occ_loss,\
"vali": [r_vali_f_mae, r_vali_f_rmse, r_vali_o_mae, r_vali_o_rmse],\
"test": [r_test_f_mae, r_test_f_rmse, r_test_o_mae, r_test_o_rmse]}
subfile = open(file_name + '/' + 'performance'+'.json','w')
json.dump(performance, subfile)
subfile.close()
#early stop
flow_std, occ_std = f_o_mean_std[1], f_o_mean_std[3]
norm_f_rmse, norm_o_rmse = vali_f_rmse/flow_std, vali_o_rmse/occ_std
norm_sum_mse = norm_f_rmse*norm_f_rmse + norm_o_rmse*norm_o_rmse
if n_mse_flow_occ > 0:
min_until_now = min([min_until_now, norm_sum_mse])
else:
min_until_now = 1000000.0
if norm_sum_mse > min_until_now:
no_decrease = no_decrease+1
else:
no_decrease = 0
if no_decrease == max_no_decrease:
print ("Early stop at the " + str(i+1) + "-th epoch")
return total_phy_flow_occ_loss, model
n_mse_flow_occ = n_mse_flow_occ + 1
print ("No_decrease: ", no_decrease)
return total_phy_flow_occ_loss, model
def tensorize(train_vali_test):
result = dict()
result["flow"] = torch.tensor(train_vali_test["flow"])
result["flow_mask"] = torch.tensor(train_vali_test["flow_mask"])
result["occupancy"] = torch.tensor(train_vali_test["occupancy"])
result["occupancy_mask"] = torch.tensor(train_vali_test["occupancy_mask"])
return result
def normalize_flow_occ(tvt, f_o_mean_std): #tvt: train, vali, test
#flow
f_mean, f_std = f_o_mean_std[0], f_o_mean_std[1]
f_mask, f = tvt["flow_mask"], tvt["flow"]
tvt["flow_mask"] = ((np.array(f_mask)-f_mean)/f_std).tolist()
tvt["flow"] = ((np.array(f)-f_mean)/f_std).tolist()
#occ
o_mean, o_std = f_o_mean_std[2], f_o_mean_std[3]
o_mask, o = tvt["occupancy_mask"], tvt["occupancy"]
tvt["occupancy_mask"] = ((np.array(o_mask)-o_mean)/o_std).tolist()
tvt["occupancy"] = ((np.array(o)-o_mean)/o_std).tolist()
return tvt
def transform_distance(d_matrix):
sigma, n_row, n_col = np.std(d_matrix), len(d_matrix), len(d_matrix[0])
sigma_square = sigma*sigma
for i in range(n_row):
for j in range(n_col):
d_i_j = d_matrix[i][j]
d_matrix[i][j] = np.exp(0.0-10000.0*d_i_j*d_i_j/sigma_square)
return d_matrix
def load_data(train_path, vali_path, test_path, sensor_adj_path, mean_std_path, sensor_id_path):
mean_std = json.load(open(mean_std_path))
f_mean, f_std, o_mean, o_std =\
mean_std["f_mean"], mean_std["f_std"], mean_std["o_mean"], mean_std["o_std"]
f_o_mean_std = [f_mean, f_std, o_mean, o_std]
train = json.load(open(train_path))
vali = json.load(open(vali_path))
test = json.load(open(test_path))
adj = json.load(open(sensor_adj_path))["adj"]
n_sensor = len(train["flow"][0])
train = tensorize(normalize_flow_occ(train, f_o_mean_std))
vali = tensorize(normalize_flow_occ(vali, f_o_mean_std))
test = tensorize(normalize_flow_occ(test, f_o_mean_std))
adj = torch.tensor(transform_distance(adj), device=device).float()
df_sensor_id = json.load(open(sensor_id_path))
sensor_length = [0.0 for i in range(n_sensor)]
for sensor in df_sensor_id:
sensor_length[df_sensor_id[sensor][0]] = df_sensor_id[sensor][3]
return train, vali, test, adj, n_sensor, f_o_mean_std, sensor_length
#6.1 load the data
time1 = time.time()
train, vali, test, adj, n_sensor, f_o_mean_std, sensor_length =\
load_data(train_path, vali_path, test_path, sensor_adj_path, mean_std_path, sensor_id_path)
time2 = time.time()
print (time2-time1)
14.948076486587524
print (len(train["flow"]))
print (len(vali["flow"]))
print (len(test["flow"]))
print (f_o_mean_std)
1536 499 500 [425.68492811748513, 254.84583261239152, 0.1814023556701015, 0.18315625109655478]
model = NMFD_GNN(n_sensor, M, hyper_model, f_o_mean_std, sensor_length, adj).to(device)
cri = nn.MSELoss()
#6.2: train the model
total_phy_flow_occ_loss, trained_model = train_process(model, cri, train, vali, test, hyper, f_o_mean_std)
# epochs 200 ----------------an epoch starts------------------- i_epoch: 0 # batch: 96 i_batch: 0.0 the loss for this batch: 1.7134186 flow loss 1.0990472 occ loss 0.6143677 time for this batch 0.8337509632110596 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.51779646 flow loss 0.25966045 occ loss 0.25813207 time for this batch 0.3999030590057373 ---------------------------------- train loss for this epoch: 0.666644
time for this epoch 49.947651386260986 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 1 # batch: 96 i_batch: 0.0 the loss for this batch: 0.38923532 flow loss 0.20001921 occ loss 0.18921247 time for this batch 0.3346080780029297 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.3179913 flow loss 0.15609379 occ loss 0.16189373 time for this batch 0.4038684368133545 ---------------------------------- train loss for this epoch: 0.34138
time for this epoch 48.5993332862854 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 2 # batch: 96 i_batch: 0.0 the loss for this batch: 0.2871922 flow loss 0.14704569 occ loss 0.14014304 time for this batch 0.4662306308746338 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.34534413 flow loss 0.14378327 occ loss 0.20155667 time for this batch 0.3985617160797119 ---------------------------------- train loss for this epoch: 0.290797
time for this epoch 47.62117147445679 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 3 # batch: 96 i_batch: 0.0 the loss for this batch: 0.25905067 flow loss 0.123558685 occ loss 0.13548848 time for this batch 0.3422975540161133 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22843058 flow loss 0.11352128 occ loss 0.11490612 time for this batch 0.40105414390563965 ---------------------------------- train loss for this epoch: 0.265842
time for this epoch 48.24851894378662 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 4 # batch: 96 i_batch: 0.0 the loss for this batch: 0.24675755 flow loss 0.11425608 occ loss 0.13249832 time for this batch 0.3576655387878418 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.23664472 flow loss 0.10530156 occ loss 0.13133985 time for this batch 0.38855624198913574 ---------------------------------- train loss for this epoch: 0.247305
time for this epoch 46.478270292282104 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 5 # batch: 96 i_batch: 0.0 the loss for this batch: 0.24319184 flow loss 0.1063535 occ loss 0.13683447 time for this batch 0.3827507495880127 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20429525 flow loss 0.08870783 occ loss 0.11558418 time for this batch 0.3249962329864502 ---------------------------------- train loss for this epoch: 0.236366
time for this epoch 48.09101986885071 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 6 # batch: 96 i_batch: 0.0 the loss for this batch: 0.2125458 flow loss 0.09404563 occ loss 0.118497126 time for this batch 0.36347293853759766 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20871449 flow loss 0.093668155 occ loss 0.115042746 time for this batch 0.42270946502685547 ---------------------------------- train loss for this epoch: 0.228613
time for this epoch 48.45596957206726 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 7 # batch: 96 i_batch: 0.0 the loss for this batch: 0.22367454 flow loss 0.09263031 occ loss 0.13104044 time for this batch 0.38973116874694824 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20158254 flow loss 0.08903083 occ loss 0.11254845 time for this batch 0.41232943534851074 ---------------------------------- train loss for this epoch: 0.221171
time for this epoch 48.989471673965454 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 8 # batch: 96 i_batch: 0.0 the loss for this batch: 0.19178802 flow loss 0.08742973 occ loss 0.10435505 time for this batch 0.3988370895385742 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19351037 flow loss 0.08214871 occ loss 0.111358225 time for this batch 0.4278287887573242 ---------------------------------- train loss for this epoch: 0.21389
time for this epoch 48.035879373550415 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 9 # batch: 96 i_batch: 0.0 the loss for this batch: 0.2522364 flow loss 0.10751678 occ loss 0.14471559 time for this batch 0.3308074474334717 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21771668 flow loss 0.08832654 occ loss 0.12938654 time for this batch 0.4020664691925049 ---------------------------------- train loss for this epoch: 0.212335
time for this epoch 47.45458745956421 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 10 # batch: 96 i_batch: 0.0 the loss for this batch: 0.2125954 flow loss 0.083210036 occ loss 0.12938206 time for this batch 0.3433499336242676 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17844406 flow loss 0.07206791 occ loss 0.10637313 time for this batch 0.42790913581848145 ---------------------------------- train loss for this epoch: 0.205483
time for this epoch 48.00741362571716 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 11 # batch: 96 i_batch: 0.0 the loss for this batch: 0.20692211 flow loss 0.08502702 occ loss 0.1218913 time for this batch 0.403548002243042 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19474114 flow loss 0.07469258 occ loss 0.12004515 time for this batch 0.4286806583404541 ---------------------------------- train loss for this epoch: 0.206505
time for this epoch 47.07732582092285 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 12 # batch: 96 i_batch: 0.0 the loss for this batch: 0.19524421 flow loss 0.08167527 occ loss 0.11356524 time for this batch 0.35141539573669434 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21691997 flow loss 0.081834264 occ loss 0.13508144 time for this batch 0.3849637508392334 ---------------------------------- train loss for this epoch: 0.201951
time for this epoch 48.33201241493225 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 13 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17980215 flow loss 0.069509044 occ loss 0.11029 time for this batch 0.37688612937927246 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20331323 flow loss 0.083243705 occ loss 0.12006552 time for this batch 0.38703203201293945 ---------------------------------- train loss for this epoch: 0.200704
time for this epoch 48.5847008228302 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 14 # batch: 96 i_batch: 0.0 the loss for this batch: 0.2008001 flow loss 0.07649641 occ loss 0.1243005 time for this batch 0.36525917053222656 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17692864 flow loss 0.07736609 occ loss 0.09955911 time for this batch 0.38483119010925293 ---------------------------------- train loss for this epoch: 0.197633
time for this epoch 46.06301426887512 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 15 # batch: 96 i_batch: 0.0 the loss for this batch: 0.2212282 flow loss 0.081321865 occ loss 0.13990197 time for this batch 0.3715963363647461 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18258475 flow loss 0.072188266 occ loss 0.110392846 time for this batch 0.42764735221862793 ---------------------------------- train loss for this epoch: 0.196878
time for this epoch 46.886202573776245 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 16 # batch: 96 i_batch: 0.0 the loss for this batch: 0.20658231 flow loss 0.07829755 occ loss 0.12828107 time for this batch 0.3718852996826172 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16844028 flow loss 0.06877511 occ loss 0.099661864 time for this batch 0.3790750503540039 ---------------------------------- train loss for this epoch: 0.195212
time for this epoch 46.04958462715149 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 17 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17858073 flow loss 0.067416884 occ loss 0.11116037 time for this batch 0.3593132495880127 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16646208 flow loss 0.071424484 occ loss 0.095034376 time for this batch 0.36090850830078125 ---------------------------------- train loss for this epoch: 0.195464
time for this epoch 47.7037672996521 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 18 # batch: 96 i_batch: 0.0 the loss for this batch: 0.19041872 flow loss 0.07303891 occ loss 0.11737645 time for this batch 0.3524601459503174 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15749629 flow loss 0.06690348 occ loss 0.090589754 time for this batch 0.40334177017211914 ---------------------------------- train loss for this epoch: 0.192508
time for this epoch 48.03777837753296 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 19 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18128638 flow loss 0.073173806 occ loss 0.10810889 time for this batch 0.3922719955444336 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17383336 flow loss 0.07140625 occ loss 0.10242397 time for this batch 0.3997964859008789 ---------------------------------- train loss for this epoch: 0.189634
time for this epoch 48.6640362739563 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 20 # batch: 96 i_batch: 0.0 the loss for this batch: 0.20526285 flow loss 0.073079385 occ loss 0.13217965 time for this batch 0.3795199394226074 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2203908 flow loss 0.077268474 occ loss 0.14311846 time for this batch 0.399810791015625 ---------------------------------- train loss for this epoch: 0.188155
time for this epoch 47.71439528465271 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 21 # batch: 96 i_batch: 0.0 the loss for this batch: 0.19079423 flow loss 0.071277246 occ loss 0.11951289 time for this batch 0.3458836078643799 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16611753 flow loss 0.069429114 occ loss 0.09668515 time for this batch 0.42494726181030273 ---------------------------------- train loss for this epoch: 0.187404
time for this epoch 48.479459047317505 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 22 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1685222 flow loss 0.072397284 occ loss 0.09612149 time for this batch 0.36983513832092285 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14372689 flow loss 0.0640567 occ loss 0.07966716 time for this batch 0.35892534255981445 ---------------------------------- train loss for this epoch: 0.186807
time for this epoch 46.38121199607849 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 23 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13217025 flow loss 0.059695 occ loss 0.07247291 time for this batch 0.3704671859741211 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.207773 flow loss 0.07253695 occ loss 0.13523234 time for this batch 0.3767087459564209 ---------------------------------- train loss for this epoch: 0.186299
time for this epoch 47.02288007736206 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 24 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17570573 flow loss 0.073237315 occ loss 0.10246532 time for this batch 0.3819608688354492 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16633452 flow loss 0.07047004 occ loss 0.0958611 time for this batch 0.4238309860229492 ---------------------------------- train loss for this epoch: 0.186715
time for this epoch 48.56561994552612 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 25 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17748275 flow loss 0.07218775 occ loss 0.10529116 time for this batch 0.37207722663879395 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16932352 flow loss 0.06806571 occ loss 0.10125476 time for this batch 0.3798258304595947 ---------------------------------- train loss for this epoch: 0.185976
time for this epoch 48.952484130859375 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 26 # batch: 96 i_batch: 0.0 the loss for this batch: 0.21004286 flow loss 0.07732836 occ loss 0.13271043 time for this batch 0.3714118003845215 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20062654 flow loss 0.07239052 occ loss 0.12823218 time for this batch 0.348660945892334 ---------------------------------- train loss for this epoch: 0.182367
time for this epoch 47.88372778892517 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 27 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18178445 flow loss 0.07314998 occ loss 0.10863085 time for this batch 0.3576667308807373 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17971875 flow loss 0.06969919 occ loss 0.110016346 time for this batch 0.39914846420288086 ---------------------------------- train loss for this epoch: 0.184534
time for this epoch 48.3538384437561 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 28 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18178108 flow loss 0.06822784 occ loss 0.113549784 time for this batch 0.3690652847290039 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16920222 flow loss 0.06917126 occ loss 0.10002691 time for this batch 0.3720078468322754 ---------------------------------- train loss for this epoch: 0.180888
time for this epoch 48.175294160842896 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 29 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17550509 flow loss 0.06991941 occ loss 0.10558196 time for this batch 0.34574055671691895 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15550466 flow loss 0.06985441 occ loss 0.085646994 time for this batch 0.40183353424072266 ---------------------------------- train loss for this epoch: 0.179525
time for this epoch 49.13167667388916 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 30 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1533584 flow loss 0.06016505 occ loss 0.09319036 time for this batch 0.3381497859954834 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1774457 flow loss 0.069750026 occ loss 0.10769175 time for this batch 0.42263102531433105 ---------------------------------- train loss for this epoch: 0.178811
time for this epoch 48.52267837524414 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 31 # batch: 96 i_batch: 0.0 the loss for this batch: 0.19227806 flow loss 0.07323714 occ loss 0.11903726 time for this batch 0.37483763694763184 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16487257 flow loss 0.06670815 occ loss 0.09816106 time for this batch 0.42584872245788574 ---------------------------------- train loss for this epoch: 0.177867
time for this epoch 49.020183801651 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 32 # batch: 96 i_batch: 0.0 the loss for this batch: 0.20069653 flow loss 0.074397504 occ loss 0.12629502 time for this batch 0.35488295555114746 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1857995 flow loss 0.07233472 occ loss 0.11346063 time for this batch 0.368729829788208 ---------------------------------- train loss for this epoch: 0.17689
time for this epoch 47.65847730636597 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 33 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17499389 flow loss 0.06825432 occ loss 0.10673634 time for this batch 0.3530924320220947 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.21191032 flow loss 0.07283493 occ loss 0.13907135 time for this batch 0.3971085548400879 ---------------------------------- train loss for this epoch: 0.177636
time for this epoch 48.206559896469116 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 34 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18965214 flow loss 0.06736433 occ loss 0.12228422 time for this batch 0.38429975509643555 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17817825 flow loss 0.068210244 occ loss 0.10996401 time for this batch 0.37871718406677246 ---------------------------------- train loss for this epoch: 0.175071
time for this epoch 48.03818464279175 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 35 # batch: 96 i_batch: 0.0 the loss for this batch: 0.22381744 flow loss 0.077297375 occ loss 0.14651608 time for this batch 0.3733823299407959 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17032814 flow loss 0.06820695 occ loss 0.10211765 time for this batch 0.43641209602355957 ---------------------------------- train loss for this epoch: 0.176129
time for this epoch 48.51428031921387 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 36 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16031425 flow loss 0.06566087 occ loss 0.09465036 time for this batch 0.33502793312072754 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18524611 flow loss 0.07008427 occ loss 0.11515802 time for this batch 0.407581090927124 ---------------------------------- train loss for this epoch: 0.176505
time for this epoch 47.326499938964844 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 37 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17465836 flow loss 0.07216455 occ loss 0.102489874 time for this batch 0.3104877471923828 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17367972 flow loss 0.06463313 occ loss 0.109043196 time for this batch 0.3634152412414551 ---------------------------------- train loss for this epoch: 0.174103
time for this epoch 48.60813093185425 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 38 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15510724 flow loss 0.06313506 occ loss 0.091968626 time for this batch 0.34978389739990234 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17629257 flow loss 0.06891415 occ loss 0.10737483 time for this batch 0.4242081642150879 ---------------------------------- train loss for this epoch: 0.17358
time for this epoch 48.41100358963013 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 39 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1388175 flow loss 0.060737617 occ loss 0.07807648 time for this batch 0.3883342742919922 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16812573 flow loss 0.067008115 occ loss 0.10111371 time for this batch 0.37655067443847656 ---------------------------------- train loss for this epoch: 0.17106
time for this epoch 47.361552715301514 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 40 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14425138 flow loss 0.058019508 occ loss 0.08622844 time for this batch 0.3766512870788574 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17656286 flow loss 0.06502725 occ loss 0.11153207 time for this batch 0.38547849655151367 ---------------------------------- train loss for this epoch: 0.170516
time for this epoch 47.32167148590088 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 41 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17073615 flow loss 0.066271774 occ loss 0.10446095 time for this batch 0.35590577125549316 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17457874 flow loss 0.06564007 occ loss 0.10893482 time for this batch 0.3773155212402344 ---------------------------------- train loss for this epoch: 0.170548
time for this epoch 46.8231635093689 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 42 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14709795 flow loss 0.057886407 occ loss 0.08920888 time for this batch 0.37665724754333496 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16136251 flow loss 0.06677687 occ loss 0.09458189 time for this batch 0.42268848419189453 ---------------------------------- train loss for this epoch: 0.172144
time for this epoch 49.1202392578125 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 43 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16932197 flow loss 0.06774019 occ loss 0.10157798 time for this batch 0.35411500930786133 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18938881 flow loss 0.07342144 occ loss 0.1159631 time for this batch 0.38460874557495117 ---------------------------------- train loss for this epoch: 0.169713
time for this epoch 49.15181541442871 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 44 # batch: 96 i_batch: 0.0 the loss for this batch: 0.2058602 flow loss 0.07481086 occ loss 0.131045 time for this batch 0.3470876216888428 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18251945 flow loss 0.0675356 occ loss 0.11497985 time for this batch 0.3957641124725342 ---------------------------------- train loss for this epoch: 0.16938
time for this epoch 48.23461866378784 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 45 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1500316 flow loss 0.06446434 occ loss 0.085563675 time for this batch 0.3556697368621826 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13453299 flow loss 0.05901866 occ loss 0.075511225 time for this batch 0.4150583744049072 ---------------------------------- train loss for this epoch: 0.16962
time for this epoch 48.9553542137146 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 46 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16255492 flow loss 0.06001512 occ loss 0.10253687 time for this batch 0.36368513107299805 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1592208 flow loss 0.06226992 occ loss 0.09694762 time for this batch 0.4228529930114746 ---------------------------------- train loss for this epoch: 0.17568
time for this epoch 47.25258994102478 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 47 # batch: 96 i_batch: 0.0 the loss for this batch: 0.22221568 flow loss 0.09781263 occ loss 0.124399565 time for this batch 0.37537670135498047 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20216396 flow loss 0.06834443 occ loss 0.13381553 time for this batch 0.37592339515686035 ---------------------------------- train loss for this epoch: 0.173076
time for this epoch 48.4588041305542 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 48 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15110889 flow loss 0.058144983 occ loss 0.09296078 time for this batch 0.3650648593902588 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18440509 flow loss 0.070714414 occ loss 0.11368654 time for this batch 0.40241408348083496 ---------------------------------- train loss for this epoch: 0.167404
time for this epoch 47.56645631790161 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 49 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16746843 flow loss 0.0683285 occ loss 0.09913571 time for this batch 0.3860299587249756 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.22204842 flow loss 0.07628816 occ loss 0.14575651 time for this batch 0.3921525478363037 ---------------------------------- train loss for this epoch: 0.167232
time for this epoch 46.01859951019287 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 50 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1395007 flow loss 0.059314758 occ loss 0.08018275 time for this batch 0.3840022087097168 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19726738 flow loss 0.06769571 occ loss 0.12956777 time for this batch 0.43278002738952637 ---------------------------------- train loss for this epoch: 0.167204
time for this epoch 47.376731872558594 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 51 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14967328 flow loss 0.06304726 occ loss 0.08662284 time for this batch 0.32445573806762695 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14936213 flow loss 0.062491015 occ loss 0.08686792 time for this batch 0.38470005989074707 ---------------------------------- train loss for this epoch: 0.165766
time for this epoch 47.998345136642456 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 52 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1301652 flow loss 0.055756986 occ loss 0.074404836 time for this batch 0.31151366233825684 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19702545 flow loss 0.07017473 occ loss 0.12684672 time for this batch 0.45148324966430664 ---------------------------------- train loss for this epoch: 0.175489
time for this epoch 47.37190127372742 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 53 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15343998 flow loss 0.06657438 occ loss 0.08686284 time for this batch 0.3490443229675293 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.164764 flow loss 0.061754234 occ loss 0.103006214 time for this batch 0.44585609436035156 ---------------------------------- train loss for this epoch: 0.170042
time for this epoch 49.10799312591553 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 54 # batch: 96 i_batch: 0.0 the loss for this batch: 0.20235604 flow loss 0.07117953 occ loss 0.1311719 time for this batch 0.34053564071655273 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17545484 flow loss 0.06335245 occ loss 0.11209884 time for this batch 0.42669224739074707 ---------------------------------- train loss for this epoch: 0.165699
time for this epoch 48.60786962509155 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 55 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1624221 flow loss 0.064007476 occ loss 0.09841062 time for this batch 0.3544185161590576 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19678624 flow loss 0.071337394 occ loss 0.12544462 time for this batch 0.3774077892303467 ---------------------------------- train loss for this epoch: 0.167242
time for this epoch 48.58372664451599 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 56 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16200142 flow loss 0.06008175 occ loss 0.10191631 time for this batch 0.3655273914337158 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16055019 flow loss 0.06686712 occ loss 0.09367955 time for this batch 0.3804168701171875 ---------------------------------- train loss for this epoch: 0.16471
time for this epoch 48.013264417648315 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 57 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1654493 flow loss 0.062798105 occ loss 0.10264769 time for this batch 0.33966946601867676 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16817605 flow loss 0.067216516 occ loss 0.10095622 time for this batch 0.4583888053894043 ---------------------------------- train loss for this epoch: 0.165366
time for this epoch 48.930795192718506 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 58 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14108025 flow loss 0.056518562 occ loss 0.08455847 time for this batch 0.3278672695159912 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15720654 flow loss 0.06105753 occ loss 0.09614504 time for this batch 0.4100980758666992 ---------------------------------- train loss for this epoch: 0.164116
time for this epoch 48.94134879112244 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 59 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17138296 flow loss 0.06594843 occ loss 0.105430804 time for this batch 0.3543379306793213 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1479333 flow loss 0.05955894 occ loss 0.08837096 time for this batch 0.4309353828430176 ---------------------------------- train loss for this epoch: 0.163366
time for this epoch 48.50791072845459 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 60 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14599729 flow loss 0.05793529 occ loss 0.08805906 time for this batch 0.3811302185058594 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16601025 flow loss 0.060948998 occ loss 0.10505745 time for this batch 0.4286015033721924 ---------------------------------- train loss for this epoch: 0.1634
time for this epoch 49.03926062583923 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 61 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15682493 flow loss 0.059764255 occ loss 0.09705733 time for this batch 0.3518846035003662 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.2103755 flow loss 0.06967225 occ loss 0.14069891 time for this batch 0.3422842025756836 ---------------------------------- train loss for this epoch: 0.16369
time for this epoch 47.48237204551697 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 62 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15299995 flow loss 0.058844604 occ loss 0.094151385 time for this batch 0.3732943534851074 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1603962 flow loss 0.062561356 occ loss 0.09783084 time for this batch 0.44892406463623047 ---------------------------------- train loss for this epoch: 0.162401
time for this epoch 48.24963712692261 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 63 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16663623 flow loss 0.060102306 occ loss 0.106530175 time for this batch 0.37777042388916016 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17610762 flow loss 0.063486025 occ loss 0.11261782 time for this batch 0.3977179527282715 ---------------------------------- train loss for this epoch: 0.162631
time for this epoch 49.09180212020874 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 64 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18015581 flow loss 0.06217591 occ loss 0.11797593 time for this batch 0.3798339366912842 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14477997 flow loss 0.058048356 occ loss 0.0867282 time for this batch 0.4409308433532715 ---------------------------------- train loss for this epoch: 0.16234
time for this epoch 48.55914616584778 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 65 # batch: 96 i_batch: 0.0 the loss for this batch: 0.12379548 flow loss 0.053947333 occ loss 0.06984481 time for this batch 0.3513803482055664 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14412844 flow loss 0.05826617 occ loss 0.085858926 time for this batch 0.42100048065185547 ---------------------------------- train loss for this epoch: 0.162344
time for this epoch 48.90671968460083 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 66 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17886212 flow loss 0.062282596 occ loss 0.116575964 time for this batch 0.351548433303833 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18462011 flow loss 0.068777695 occ loss 0.11583843 time for this batch 0.4202253818511963 ---------------------------------- train loss for this epoch: 0.164556
time for this epoch 48.27514863014221 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 67 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16818981 flow loss 0.068382904 occ loss 0.099803485 time for this batch 0.3527536392211914 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16940564 flow loss 0.06530964 occ loss 0.10409217 time for this batch 0.37507152557373047 ---------------------------------- train loss for this epoch: 0.163829
time for this epoch 48.57910346984863 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 68 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16885494 flow loss 0.06000575 occ loss 0.10884523 time for this batch 0.37653088569641113 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1558515 flow loss 0.060984846 occ loss 0.0948629 time for this batch 0.41900157928466797 ---------------------------------- train loss for this epoch: 0.160189
time for this epoch 48.00223708152771 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 69 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16449969 flow loss 0.06281728 occ loss 0.10167863 time for this batch 0.3294677734375 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17912185 flow loss 0.06581363 occ loss 0.11330428 time for this batch 0.39698123931884766 ---------------------------------- train loss for this epoch: 0.161657
time for this epoch 46.77592945098877 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 70 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1633225 flow loss 0.061473932 occ loss 0.101844616 time for this batch 0.3639068603515625 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19304112 flow loss 0.06674568 occ loss 0.12629084 time for this batch 0.37119460105895996 ---------------------------------- train loss for this epoch: 0.161723
time for this epoch 47.34547162055969 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 71 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1819989 flow loss 0.06231061 occ loss 0.11968431 time for this batch 0.48946309089660645 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13934657 flow loss 0.05623801 occ loss 0.08310519 time for this batch 0.38545656204223633 ---------------------------------- train loss for this epoch: 0.161293
time for this epoch 46.30097150802612 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 72 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16320747 flow loss 0.06203542 occ loss 0.101168245 time for this batch 0.3555269241333008 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16515255 flow loss 0.060773008 occ loss 0.10437541 time for this batch 0.3433253765106201 ---------------------------------- train loss for this epoch: 0.159651
time for this epoch 46.33944249153137 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 73 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15304148 flow loss 0.058992483 occ loss 0.09404557 time for this batch 0.34882569313049316 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20429952 flow loss 0.06498496 occ loss 0.1393099 time for this batch 0.4361252784729004 ---------------------------------- train loss for this epoch: 0.160222
time for this epoch 48.874319553375244 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 74 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1502972 flow loss 0.057655223 occ loss 0.09263848 time for this batch 0.36963987350463867 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14534463 flow loss 0.054627705 occ loss 0.090713926 time for this batch 0.38800597190856934 ---------------------------------- train loss for this epoch: 0.158143
time for this epoch 47.59178280830383 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 75 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16653363 flow loss 0.064314276 occ loss 0.10221544 time for this batch 0.30694007873535156 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14741513 flow loss 0.05857381 occ loss 0.088837944 time for this batch 0.3862731456756592 ---------------------------------- train loss for this epoch: 0.158891
time for this epoch 46.95220708847046 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 76 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14054984 flow loss 0.060432997 occ loss 0.08011308 time for this batch 0.3370475769042969 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16504493 flow loss 0.061502505 occ loss 0.103539184 time for this batch 0.41141653060913086 ---------------------------------- train loss for this epoch: 0.159407
time for this epoch 49.118690490722656 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 77 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15557976 flow loss 0.060244463 occ loss 0.09533172 time for this batch 0.3860313892364502 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.09179335 flow loss 0.044492826 occ loss 0.047298595 time for this batch 0.3787364959716797 ---------------------------------- train loss for this epoch: 0.159733
time for this epoch 48.02308130264282 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 78 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17141105 flow loss 0.062380843 occ loss 0.109026484 time for this batch 0.43531107902526855 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17656429 flow loss 0.062957935 occ loss 0.11360227 time for this batch 0.417377233505249 ---------------------------------- train loss for this epoch: 0.158151
time for this epoch 49.558494567871094 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 79 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15221302 flow loss 0.06030764 occ loss 0.09190143 time for this batch 0.38852477073669434 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18145454 flow loss 0.06546498 occ loss 0.1159855 time for this batch 0.3756732940673828 ---------------------------------- train loss for this epoch: 0.158387
time for this epoch 47.055466651916504 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 80 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14434236 flow loss 0.05477703 occ loss 0.08956174 time for this batch 0.3565237522125244 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13532852 flow loss 0.05567025 occ loss 0.07965465 time for this batch 0.39423084259033203 ---------------------------------- train loss for this epoch: 0.156832
time for this epoch 43.95597767829895 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 81 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14741363 flow loss 0.055525746 occ loss 0.0918845 time for this batch 0.34742093086242676 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13627635 flow loss 0.0554684 occ loss 0.08080489 time for this batch 0.34770894050598145 ---------------------------------- train loss for this epoch: 0.157059
time for this epoch 47.59606170654297 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 82 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14671025 flow loss 0.057734597 occ loss 0.08897226 time for this batch 0.349320650100708 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1260686 flow loss 0.05530423 occ loss 0.07076085 time for this batch 0.3963809013366699 ---------------------------------- train loss for this epoch: 0.157147
time for this epoch 48.56378698348999 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 83 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16160542 flow loss 0.059492934 occ loss 0.102108754 time for this batch 0.3604090213775635 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.106584735 flow loss 0.048291985 occ loss 0.05829035 time for this batch 0.36638593673706055 ---------------------------------- train loss for this epoch: 0.158334
time for this epoch 47.07132935523987 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 84 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14264031 flow loss 0.053693615 occ loss 0.08894326 time for this batch 0.3861382007598877 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17757611 flow loss 0.059780207 occ loss 0.11779206 time for this batch 0.3683946132659912 ---------------------------------- train loss for this epoch: 0.15691
time for this epoch 48.94051480293274 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 85 # batch: 96 i_batch: 0.0 the loss for this batch: 0.10570159 flow loss 0.049293537 occ loss 0.05640537 time for this batch 0.35743188858032227 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.122541964 flow loss 0.050529774 occ loss 0.07200907 time for this batch 0.4308180809020996 ---------------------------------- train loss for this epoch: 0.156633
time for this epoch 48.42792582511902 No_decrease: 8 ----------------an epoch starts------------------- i_epoch: 86 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13901012 flow loss 0.053947717 occ loss 0.08505918 time for this batch 0.3635077476501465 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19122322 flow loss 0.06428098 occ loss 0.12693852 time for this batch 0.4046196937561035 ---------------------------------- train loss for this epoch: 0.157955
time for this epoch 48.14636182785034 No_decrease: 9 ----------------an epoch starts------------------- i_epoch: 87 # batch: 96 i_batch: 0.0 the loss for this batch: 0.122390576 flow loss 0.053212784 occ loss 0.069174774 time for this batch 0.3183915615081787 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15431857 flow loss 0.058872417 occ loss 0.09544258 time for this batch 0.4207127094268799 ---------------------------------- train loss for this epoch: 0.156352
time for this epoch 48.11696982383728 No_decrease: 10 ----------------an epoch starts------------------- i_epoch: 88 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13351944 flow loss 0.052580718 occ loss 0.080935635 time for this batch 0.33232951164245605 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13713503 flow loss 0.053832527 occ loss 0.08329955 time for this batch 0.38386058807373047 ---------------------------------- train loss for this epoch: 0.156407
time for this epoch 47.90018916130066 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 89 # batch: 96 i_batch: 0.0 the loss for this batch: 0.12978147 flow loss 0.051611856 occ loss 0.07816628 time for this batch 0.3499164581298828 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18425357 flow loss 0.06555784 occ loss 0.118691586 time for this batch 0.4010899066925049 ---------------------------------- train loss for this epoch: 0.156299
time for this epoch 46.603870153427124 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 90 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14668864 flow loss 0.054683022 occ loss 0.092002414 time for this batch 0.36347007751464844 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16588266 flow loss 0.059996277 occ loss 0.10588232 time for this batch 0.38941431045532227 ---------------------------------- train loss for this epoch: 0.156521
time for this epoch 49.33804726600647 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 91 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18066716 flow loss 0.060931224 occ loss 0.11973218 time for this batch 0.39290833473205566 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13060686 flow loss 0.052471515 occ loss 0.078131765 time for this batch 0.3804457187652588 ---------------------------------- train loss for this epoch: 0.1555
time for this epoch 48.719746828079224 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 92 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16360791 flow loss 0.061375357 occ loss 0.10222871 time for this batch 0.3340141773223877 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15322076 flow loss 0.056563396 occ loss 0.096653976 time for this batch 0.3909025192260742 ---------------------------------- train loss for this epoch: 0.155835
time for this epoch 47.650012731552124 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 93 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17350234 flow loss 0.06302252 occ loss 0.11047617 time for this batch 0.3594996929168701 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15468298 flow loss 0.057049483 occ loss 0.097629964 time for this batch 0.4230234622955322 ---------------------------------- train loss for this epoch: 0.155595
time for this epoch 48.256937980651855 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 94 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15919222 flow loss 0.05492577 occ loss 0.10426311 time for this batch 0.4753851890563965 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17866047 flow loss 0.06393842 occ loss 0.11471796 time for this batch 0.3918893337249756 ---------------------------------- train loss for this epoch: 0.155405
time for this epoch 47.46809959411621 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 95 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15942144 flow loss 0.060710292 occ loss 0.098707125 time for this batch 0.32202935218811035 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14200701 flow loss 0.057956804 occ loss 0.08404669 time for this batch 0.31537342071533203 ---------------------------------- train loss for this epoch: 0.155794
time for this epoch 42.270047664642334 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 96 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16466129 flow loss 0.06100275 occ loss 0.10365469 time for this batch 0.2802438735961914 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16394056 flow loss 0.05643364 occ loss 0.10750326 time for this batch 0.34631824493408203 ---------------------------------- train loss for this epoch: 0.154949
time for this epoch 42.09691143035889 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 97 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1489404 flow loss 0.054535206 occ loss 0.09440174 time for this batch 0.3399941921234131 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1838057 flow loss 0.06318152 occ loss 0.120619826 time for this batch 0.3516111373901367 ---------------------------------- train loss for this epoch: 0.154523
time for this epoch 42.72897458076477 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 98 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13942412 flow loss 0.0563885 occ loss 0.08303193 time for this batch 0.33379411697387695 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15298492 flow loss 0.05965528 occ loss 0.09332623 time for this batch 0.3623507022857666 ---------------------------------- train loss for this epoch: 0.154782
time for this epoch 47.80368137359619 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 99 # batch: 96 i_batch: 0.0 the loss for this batch: 0.19195934 flow loss 0.0643954 occ loss 0.12755965 time for this batch 0.34965991973876953 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14187121 flow loss 0.057175778 occ loss 0.084692195 time for this batch 0.40005922317504883 ---------------------------------- train loss for this epoch: 0.154975
time for this epoch 46.84027624130249 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 100 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14591907 flow loss 0.056475382 occ loss 0.089439906 time for this batch 0.472919225692749 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14867939 flow loss 0.058819205 occ loss 0.08985658 time for this batch 0.3612802028656006 ---------------------------------- train loss for this epoch: 0.153915
time for this epoch 46.690550088882446 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 101 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1304063 flow loss 0.051875405 occ loss 0.0785278 time for this batch 0.36301255226135254 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14935437 flow loss 0.054613363 occ loss 0.09473769 time for this batch 0.41179585456848145 ---------------------------------- train loss for this epoch: 0.154126
time for this epoch 48.23427200317383 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 102 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1377535 flow loss 0.056589488 occ loss 0.08115992 time for this batch 0.33768534660339355 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16355059 flow loss 0.058376454 occ loss 0.105170324 time for this batch 0.4018535614013672 ---------------------------------- train loss for this epoch: 0.15382
time for this epoch 48.383814573287964 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 103 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16658063 flow loss 0.06129822 occ loss 0.1052786 time for this batch 0.3965463638305664 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1433267 flow loss 0.055553645 occ loss 0.08776961 time for this batch 0.43162965774536133 ---------------------------------- train loss for this epoch: 0.153142
time for this epoch 48.45048451423645 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 104 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16333833 flow loss 0.058407564 occ loss 0.10492718 time for this batch 0.3228442668914795 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13046542 flow loss 0.054536216 occ loss 0.075925894 time for this batch 0.4259371757507324 ---------------------------------- train loss for this epoch: 0.155076
time for this epoch 48.03770089149475 No_decrease: 8 ----------------an epoch starts------------------- i_epoch: 105 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17693822 flow loss 0.06800005 occ loss 0.10893399 time for this batch 0.3591039180755615 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1688739 flow loss 0.05860214 occ loss 0.11026794 time for this batch 0.3851449489593506 ---------------------------------- train loss for this epoch: 0.154649
time for this epoch 48.05649423599243 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 106 # batch: 96 i_batch: 0.0 the loss for this batch: 0.183798 flow loss 0.06277251 occ loss 0.12102138 time for this batch 0.3096141815185547 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13524774 flow loss 0.053556755 occ loss 0.08168778 time for this batch 0.4092104434967041 ---------------------------------- train loss for this epoch: 0.153283
time for this epoch 54.439125776290894 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 107 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1149971 flow loss 0.04983301 occ loss 0.065161295 time for this batch 0.38738512992858887 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16430652 flow loss 0.057795264 occ loss 0.10650746 time for this batch 0.3842334747314453 ---------------------------------- train loss for this epoch: 0.1534
time for this epoch 51.8572781085968 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 108 # batch: 96 i_batch: 0.0 the loss for this batch: 0.12346069 flow loss 0.048478093 occ loss 0.07497962 time for this batch 0.5159425735473633 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16322987 flow loss 0.05634631 occ loss 0.10688014 time for this batch 1.4420912265777588 ---------------------------------- train loss for this epoch: 0.153063
time for this epoch 75.31813931465149 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 109 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17466755 flow loss 0.060741894 occ loss 0.11392129 time for this batch 0.3504760265350342 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13519986 flow loss 0.050977238 occ loss 0.0842195 time for this batch 0.44594883918762207 ---------------------------------- train loss for this epoch: 0.153264
time for this epoch 48.193124532699585 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 110 # batch: 96 i_batch: 0.0 the loss for this batch: 0.19148584 flow loss 0.06421292 occ loss 0.12726875 time for this batch 0.36832547187805176 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.10854745 flow loss 0.044212658 occ loss 0.06433223 time for this batch 0.4330925941467285 ---------------------------------- train loss for this epoch: 0.152742
time for this epoch 48.091710805892944 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 111 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17226669 flow loss 0.059151605 occ loss 0.113111004 time for this batch 0.33197021484375 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16244806 flow loss 0.055066045 occ loss 0.107378125 time for this batch 0.41382861137390137 ---------------------------------- train loss for this epoch: 0.153208
time for this epoch 47.75213384628296 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 112 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18823104 flow loss 0.06625065 occ loss 0.121975936 time for this batch 0.3885006904602051 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1681736 flow loss 0.06091124 occ loss 0.10725863 time for this batch 0.3857839107513428 ---------------------------------- train loss for this epoch: 0.152679
time for this epoch 49.839924573898315 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 113 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16741836 flow loss 0.06086225 occ loss 0.106552124 time for this batch 0.382922887802124 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14497061 flow loss 0.05519462 occ loss 0.0897723 time for this batch 0.3604443073272705 ---------------------------------- train loss for this epoch: 0.152967
time for this epoch 46.95431661605835 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 114 # batch: 96 i_batch: 0.0 the loss for this batch: 0.12602952 flow loss 0.050280012 occ loss 0.07574618 time for this batch 0.4330275058746338 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13153772 flow loss 0.054329023 occ loss 0.07720513 time for this batch 0.46374964714050293 ---------------------------------- train loss for this epoch: 0.152005
time for this epoch 46.88751220703125 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 115 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16255175 flow loss 0.055763863 occ loss 0.10678411 time for this batch 0.34894371032714844 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.18300898 flow loss 0.062251166 occ loss 0.12075362 time for this batch 0.4055664539337158 ---------------------------------- train loss for this epoch: 0.153055
time for this epoch 48.50746941566467 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 116 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14684448 flow loss 0.055526275 occ loss 0.0913146 time for this batch 0.3440675735473633 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17899498 flow loss 0.06373162 occ loss 0.11525949 time for this batch 0.43737077713012695 ---------------------------------- train loss for this epoch: 0.153985
time for this epoch 48.2318160533905 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 117 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13286278 flow loss 0.053624213 occ loss 0.07923503 time for this batch 0.31334424018859863 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15855844 flow loss 0.055797502 occ loss 0.10275768 time for this batch 0.4033377170562744 ---------------------------------- train loss for this epoch: 0.153032
time for this epoch 48.607205390930176 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 118 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13103913 flow loss 0.04999762 occ loss 0.08103849 time for this batch 0.4180934429168701 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19345874 flow loss 0.06380035 occ loss 0.12965429 time for this batch 0.441882848739624 ---------------------------------- train loss for this epoch: 0.151938
time for this epoch 48.40232253074646 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 119 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14836285 flow loss 0.05572964 occ loss 0.092629574 time for this batch 0.36736559867858887 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14858916 flow loss 0.057105444 occ loss 0.09147999 time for this batch 0.3897860050201416 ---------------------------------- train loss for this epoch: 0.151848
time for this epoch 49.22481966018677 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 120 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13345353 flow loss 0.052221518 occ loss 0.08122849 time for this batch 0.4228518009185791 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1808026 flow loss 0.061536387 occ loss 0.11926206 time for this batch 0.41322827339172363 ---------------------------------- train loss for this epoch: 0.15176
time for this epoch 49.416242837905884 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 121 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17228964 flow loss 0.060158078 occ loss 0.11212768 time for this batch 0.3858671188354492 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17762356 flow loss 0.060714982 occ loss 0.11690427 time for this batch 0.41666746139526367 ---------------------------------- train loss for this epoch: 0.153452
time for this epoch 48.580647706985474 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 122 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1507359 flow loss 0.053741932 occ loss 0.096990585 time for this batch 0.367295503616333 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.11658298 flow loss 0.049762975 occ loss 0.066816844 time for this batch 0.31035852432250977 ---------------------------------- train loss for this epoch: 0.150996
time for this epoch 48.92133712768555 No_decrease: 8 ----------------an epoch starts------------------- i_epoch: 123 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15034945 flow loss 0.052407537 occ loss 0.09793838 time for this batch 0.39115214347839355 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14307481 flow loss 0.058761332 occ loss 0.08431017 time for this batch 0.3783547878265381 ---------------------------------- train loss for this epoch: 0.153087
time for this epoch 47.55804705619812 No_decrease: 9 ----------------an epoch starts------------------- i_epoch: 124 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15911566 flow loss 0.06062068 occ loss 0.098491125 time for this batch 0.35712218284606934 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13844518 flow loss 0.054627094 occ loss 0.083814636 time for this batch 0.3547642230987549 ---------------------------------- train loss for this epoch: 0.153342
time for this epoch 47.53474736213684 No_decrease: 10 ----------------an epoch starts------------------- i_epoch: 125 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15517087 flow loss 0.056642205 occ loss 0.098524675 time for this batch 0.3610706329345703 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15226923 flow loss 0.05580119 occ loss 0.09646458 time for this batch 0.34873175621032715 ---------------------------------- train loss for this epoch: 0.151029
time for this epoch 47.303492069244385 No_decrease: 11 ----------------an epoch starts------------------- i_epoch: 126 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18501183 flow loss 0.06406085 occ loss 0.120946504 time for this batch 0.3747522830963135 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.12147129 flow loss 0.050724506 occ loss 0.07074384 time for this batch 0.36142802238464355 ---------------------------------- train loss for this epoch: 0.150597
time for this epoch 47.92799758911133 No_decrease: 12 ----------------an epoch starts------------------- i_epoch: 127 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1391087 flow loss 0.054089043 occ loss 0.08501614 time for this batch 0.37507128715515137 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.164035 flow loss 0.061666444 occ loss 0.10236463 time for this batch 0.4217045307159424 ---------------------------------- train loss for this epoch: 0.151178
time for this epoch 47.78583908081055 No_decrease: 13 ----------------an epoch starts------------------- i_epoch: 128 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17402917 flow loss 0.06078452 occ loss 0.11324035 time for this batch 0.43570899963378906 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14160758 flow loss 0.05520681 occ loss 0.08639732 time for this batch 0.42159199714660645 ---------------------------------- train loss for this epoch: 0.151494
time for this epoch 48.13316750526428 No_decrease: 14 ----------------an epoch starts------------------- i_epoch: 129 # batch: 96 i_batch: 0.0 the loss for this batch: 0.12824759 flow loss 0.05105027 occ loss 0.07719419 time for this batch 0.33087849617004395 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13342027 flow loss 0.048547875 occ loss 0.08486916 time for this batch 0.4084150791168213 ---------------------------------- train loss for this epoch: 0.151749
time for this epoch 48.350919246673584 No_decrease: 15 ----------------an epoch starts------------------- i_epoch: 130 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15414113 flow loss 0.058416277 occ loss 0.09572097 time for this batch 0.38320112228393555 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.20102887 flow loss 0.06430148 occ loss 0.1367229 time for this batch 0.428631067276001 ---------------------------------- train loss for this epoch: 0.15159
time for this epoch 49.86769485473633 No_decrease: 16 ----------------an epoch starts------------------- i_epoch: 131 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15905717 flow loss 0.05454856 occ loss 0.10450499 time for this batch 0.3558349609375 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13805324 flow loss 0.052560758 occ loss 0.08548897 time for this batch 0.431743860244751 ---------------------------------- train loss for this epoch: 0.150836
time for this epoch 48.5361111164093 No_decrease: 17 ----------------an epoch starts------------------- i_epoch: 132 # batch: 96 i_batch: 0.0 the loss for this batch: 0.137541 flow loss 0.05250831 occ loss 0.08502925 time for this batch 0.37985754013061523 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.12338389 flow loss 0.052908577 occ loss 0.07047258 time for this batch 0.34657979011535645 ---------------------------------- train loss for this epoch: 0.150395
time for this epoch 46.31313395500183 No_decrease: 18 ----------------an epoch starts------------------- i_epoch: 133 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13934125 flow loss 0.053584315 occ loss 0.08575317 time for this batch 0.3800826072692871 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17148034 flow loss 0.05887843 occ loss 0.11259807 time for this batch 0.43180084228515625 ---------------------------------- train loss for this epoch: 0.151081
time for this epoch 49.412429332733154 No_decrease: 19 ----------------an epoch starts------------------- i_epoch: 134 # batch: 96 i_batch: 0.0 the loss for this batch: 0.11723593 flow loss 0.047662918 occ loss 0.06957013 time for this batch 0.4618384838104248 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16529046 flow loss 0.066635184 occ loss 0.098651305 time for this batch 0.46510982513427734 ---------------------------------- train loss for this epoch: 0.16402
time for this epoch 49.32848501205444 No_decrease: 20 ----------------an epoch starts------------------- i_epoch: 135 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1326901 flow loss 0.05296177 occ loss 0.07972485 time for this batch 0.34221625328063965 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15021199 flow loss 0.05327739 occ loss 0.09693108 time for this batch 0.42858004570007324 ---------------------------------- train loss for this epoch: 0.150988
time for this epoch 48.66980242729187 No_decrease: 21 ----------------an epoch starts------------------- i_epoch: 136 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15322165 flow loss 0.05645263 occ loss 0.09676574 time for this batch 0.32892704010009766 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14451122 flow loss 0.052126683 occ loss 0.09238113 time for this batch 0.42034006118774414 ---------------------------------- train loss for this epoch: 0.151694
time for this epoch 48.82072043418884 No_decrease: 22 ----------------an epoch starts------------------- i_epoch: 137 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16053507 flow loss 0.059061535 occ loss 0.10146975 time for this batch 0.36021924018859863 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15984294 flow loss 0.058162685 occ loss 0.10167618 time for this batch 0.40193676948547363 ---------------------------------- train loss for this epoch: 0.149227
time for this epoch 48.66748380661011 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 138 # batch: 96 i_batch: 0.0 the loss for this batch: 0.12337299 flow loss 0.050123047 occ loss 0.07324664 time for this batch 0.3686356544494629 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1344463 flow loss 0.053563293 occ loss 0.080879405 time for this batch 0.3496818542480469 ---------------------------------- train loss for this epoch: 0.148407
time for this epoch 46.99736714363098 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 139 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1750883 flow loss 0.06063973 occ loss 0.11444433 time for this batch 0.34031081199645996 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14804655 flow loss 0.05519394 occ loss 0.092848636 time for this batch 0.4165029525756836 ---------------------------------- train loss for this epoch: 0.149991
time for this epoch 46.832175493240356 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 140 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14024787 flow loss 0.056243718 occ loss 0.08400105 time for this batch 0.3664124011993408 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13723361 flow loss 0.05043841 occ loss 0.08679161 time for this batch 0.42702674865722656 ---------------------------------- train loss for this epoch: 0.150308
time for this epoch 48.53786873817444 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 141 # batch: 96 i_batch: 0.0 the loss for this batch: 0.18540817 flow loss 0.06156114 occ loss 0.123843074 time for this batch 0.3714311122894287 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.10475438 flow loss 0.047331482 occ loss 0.057419967 time for this batch 0.4130275249481201 ---------------------------------- train loss for this epoch: 0.151269
time for this epoch 48.701456785202026 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 142 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15230897 flow loss 0.056988478 occ loss 0.09531672 time for this batch 0.34523558616638184 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.19590102 flow loss 0.06482755 occ loss 0.131069 time for this batch 0.4243888854980469 ---------------------------------- train loss for this epoch: 0.149924
time for this epoch 49.0816810131073 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 143 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13700391 flow loss 0.053851314 occ loss 0.08314891 time for this batch 0.3692820072174072 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13628751 flow loss 0.050823182 occ loss 0.08546103 time for this batch 0.37779831886291504 ---------------------------------- train loss for this epoch: 0.148676
time for this epoch 47.69076895713806 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 144 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14415637 flow loss 0.05302428 occ loss 0.09112851 time for this batch 0.3416774272918701 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17605577 flow loss 0.062204383 occ loss 0.11384788 time for this batch 0.39040708541870117 ---------------------------------- train loss for this epoch: 0.15138
time for this epoch 47.328773736953735 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 145 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16048549 flow loss 0.059191722 occ loss 0.1012898 time for this batch 0.36072230339050293 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.17336866 flow loss 0.05730767 occ loss 0.11605741 time for this batch 0.3767120838165283 ---------------------------------- train loss for this epoch: 0.149794
time for this epoch 46.05568766593933 No_decrease: 8 ----------------an epoch starts------------------- i_epoch: 146 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1694465 flow loss 0.05674397 occ loss 0.1126988 time for this batch 0.4194481372833252 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15426971 flow loss 0.05687571 occ loss 0.0973901 time for this batch 0.3546257019042969 ---------------------------------- train loss for this epoch: 0.149489
time for this epoch 48.79438352584839 No_decrease: 9 ----------------an epoch starts------------------- i_epoch: 147 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13673115 flow loss 0.050575092 occ loss 0.08615239 time for this batch 0.34584522247314453 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16329494 flow loss 0.058794074 occ loss 0.10449685 time for this batch 0.41993141174316406 ---------------------------------- train loss for this epoch: 0.148699
time for this epoch 48.36736035346985 No_decrease: 10 ----------------an epoch starts------------------- i_epoch: 148 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15239167 flow loss 0.05571376 occ loss 0.096674286 time for this batch 0.4483973979949951 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13568044 flow loss 0.051957175 occ loss 0.08371973 time for this batch 0.4216299057006836 ---------------------------------- train loss for this epoch: 0.149206
time for this epoch 48.39578890800476 No_decrease: 11 ----------------an epoch starts------------------- i_epoch: 149 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15056293 flow loss 0.054045547 occ loss 0.09651371 time for this batch 0.39874815940856934 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.11048811 flow loss 0.0468958 occ loss 0.063589394 time for this batch 0.3790419101715088 ---------------------------------- train loss for this epoch: 0.148793
time for this epoch 48.63117527961731 No_decrease: 12 ----------------an epoch starts------------------- i_epoch: 150 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13555527 flow loss 0.04876104 occ loss 0.0867911 time for this batch 0.3571956157684326 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1588915 flow loss 0.054990582 occ loss 0.10389668 time for this batch 0.3586161136627197 ---------------------------------- train loss for this epoch: 0.143306
time for this epoch 48.968064308166504 No_decrease: 0 ----------------an epoch starts------------------- i_epoch: 151 # batch: 96 i_batch: 0.0 the loss for this batch: 0.11689623 flow loss 0.045602232 occ loss 0.07129066 time for this batch 0.35057735443115234 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1715864 flow loss 0.058024228 occ loss 0.11355807 time for this batch 0.41805481910705566 ---------------------------------- train loss for this epoch: 0.14186
time for this epoch 46.93658137321472 No_decrease: 1 ----------------an epoch starts------------------- i_epoch: 152 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1425421 flow loss 0.052010484 occ loss 0.090528026 time for this batch 0.29692625999450684 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16404937 flow loss 0.057037167 occ loss 0.10700787 time for this batch 0.42299652099609375 ---------------------------------- train loss for this epoch: 0.141622
time for this epoch 49.510366916656494 No_decrease: 2 ----------------an epoch starts------------------- i_epoch: 153 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14110069 flow loss 0.053462032 occ loss 0.08763505 time for this batch 0.3752443790435791 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14418274 flow loss 0.05281753 occ loss 0.091361426 time for this batch 0.4225616455078125 ---------------------------------- train loss for this epoch: 0.141313
time for this epoch 48.19930076599121 No_decrease: 3 ----------------an epoch starts------------------- i_epoch: 154 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1492642 flow loss 0.056301307 occ loss 0.09295891 time for this batch 0.32993268966674805 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1332174 flow loss 0.050122634 occ loss 0.08309128 time for this batch 0.436403751373291 ---------------------------------- train loss for this epoch: 0.141158
time for this epoch 47.690552711486816 No_decrease: 4 ----------------an epoch starts------------------- i_epoch: 155 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14847776 flow loss 0.052358687 occ loss 0.09611527 time for this batch 0.3659815788269043 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.120160624 flow loss 0.043309852 occ loss 0.07684773 time for this batch 0.3982863426208496 ---------------------------------- train loss for this epoch: 0.14186
time for this epoch 48.41939449310303 No_decrease: 5 ----------------an epoch starts------------------- i_epoch: 156 # batch: 96 i_batch: 0.0 the loss for this batch: 0.11521742 flow loss 0.043076817 occ loss 0.07213783 time for this batch 0.3396167755126953 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13693623 flow loss 0.047813963 occ loss 0.08911875 time for this batch 0.36861443519592285 ---------------------------------- train loss for this epoch: 0.141363
time for this epoch 47.951122522354126 No_decrease: 6 ----------------an epoch starts------------------- i_epoch: 157 # batch: 96 i_batch: 0.0 the loss for this batch: 0.103496626 flow loss 0.042418074 occ loss 0.06107583 time for this batch 0.4069526195526123 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15236458 flow loss 0.050573174 occ loss 0.10178769 time for this batch 0.36821579933166504 ---------------------------------- train loss for this epoch: 0.140886
time for this epoch 45.5228431224823 No_decrease: 7 ----------------an epoch starts------------------- i_epoch: 158 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14644124 flow loss 0.050983492 occ loss 0.09545395 time for this batch 0.28082752227783203 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14728628 flow loss 0.05183949 occ loss 0.09544319 time for this batch 0.40016698837280273 ---------------------------------- train loss for this epoch: 0.140881
time for this epoch 44.79819297790527 No_decrease: 8 ----------------an epoch starts------------------- i_epoch: 159 # batch: 96 i_batch: 0.0 the loss for this batch: 0.12012824 flow loss 0.046982106 occ loss 0.073142655 time for this batch 0.2748756408691406 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14835136 flow loss 0.052588236 occ loss 0.09575908 time for this batch 0.35581374168395996 ---------------------------------- train loss for this epoch: 0.141031
time for this epoch 43.18134164810181 No_decrease: 9 ----------------an epoch starts------------------- i_epoch: 160 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13372913 flow loss 0.04997813 occ loss 0.08374728 time for this batch 0.31995177268981934 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1601174 flow loss 0.05494273 occ loss 0.10517094 time for this batch 0.40383315086364746 ---------------------------------- train loss for this epoch: 0.140781
time for this epoch 44.06944227218628 No_decrease: 10 ----------------an epoch starts------------------- i_epoch: 161 # batch: 96 i_batch: 0.0 the loss for this batch: 0.09086891 flow loss 0.03804787 occ loss 0.052818526 time for this batch 0.3625476360321045 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14746961 flow loss 0.052044205 occ loss 0.095421456 time for this batch 0.4116969108581543 ---------------------------------- train loss for this epoch: 0.140803
time for this epoch 44.25931406021118 No_decrease: 11 ----------------an epoch starts------------------- i_epoch: 162 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17450859 flow loss 0.058391597 occ loss 0.11611271 time for this batch 0.3290250301361084 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15234311 flow loss 0.0517727 occ loss 0.10056648 time for this batch 0.379960298538208 ---------------------------------- train loss for this epoch: 0.140778
time for this epoch 44.29066324234009 No_decrease: 12 ----------------an epoch starts------------------- i_epoch: 163 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1067864 flow loss 0.045683444 occ loss 0.061099738 time for this batch 0.34609007835388184 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15447095 flow loss 0.05416426 occ loss 0.10030269 time for this batch 0.38927459716796875 ---------------------------------- train loss for this epoch: 0.140473
time for this epoch 45.47923970222473 No_decrease: 13 ----------------an epoch starts------------------- i_epoch: 164 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14567664 flow loss 0.056109726 occ loss 0.089563 time for this batch 0.3171398639678955 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13705681 flow loss 0.05070455 occ loss 0.08634868 time for this batch 0.3407881259918213 ---------------------------------- train loss for this epoch: 0.140786
time for this epoch 44.639572620391846 No_decrease: 14 ----------------an epoch starts------------------- i_epoch: 165 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15258169 flow loss 0.05284744 occ loss 0.09973032 time for this batch 0.30994749069213867 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1497818 flow loss 0.054342132 occ loss 0.09543574 time for this batch 0.41049933433532715 ---------------------------------- train loss for this epoch: 0.140685
time for this epoch 42.31248331069946 No_decrease: 15 ----------------an epoch starts------------------- i_epoch: 166 # batch: 96 i_batch: 0.0 the loss for this batch: 0.1434114 flow loss 0.05293727 occ loss 0.09047021 time for this batch 0.36052680015563965 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.12529981 flow loss 0.049442824 occ loss 0.07585344 time for this batch 0.4349403381347656 ---------------------------------- train loss for this epoch: 0.140705
time for this epoch 44.39147162437439 No_decrease: 16 ----------------an epoch starts------------------- i_epoch: 167 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13913238 flow loss 0.051208746 occ loss 0.08791997 time for this batch 0.270003080368042 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16061507 flow loss 0.052580856 occ loss 0.1080302 time for this batch 0.29834556579589844 ---------------------------------- train loss for this epoch: 0.140447
time for this epoch 44.205933570861816 No_decrease: 17 ----------------an epoch starts------------------- i_epoch: 168 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13484085 flow loss 0.04779384 occ loss 0.087043606 time for this batch 0.35243844985961914 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1416411 flow loss 0.052629292 occ loss 0.08900834 time for this batch 0.37276530265808105 ---------------------------------- train loss for this epoch: 0.140878
time for this epoch 43.29487705230713 No_decrease: 18 ----------------an epoch starts------------------- i_epoch: 169 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14793277 flow loss 0.05334263 occ loss 0.09458605 time for this batch 0.3545382022857666 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1315937 flow loss 0.04822034 occ loss 0.08336967 time for this batch 0.38369154930114746 ---------------------------------- train loss for this epoch: 0.140342
time for this epoch 44.275460720062256 No_decrease: 19 ----------------an epoch starts------------------- i_epoch: 170 # batch: 96 i_batch: 0.0 the loss for this batch: 0.17096914 flow loss 0.053388275 occ loss 0.11757699 time for this batch 0.30662107467651367 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1649423 flow loss 0.052961856 occ loss 0.11197648 time for this batch 0.30416393280029297 ---------------------------------- train loss for this epoch: 0.14018
time for this epoch 44.46618032455444 No_decrease: 20 ----------------an epoch starts------------------- i_epoch: 171 # batch: 96 i_batch: 0.0 the loss for this batch: 0.12874214 flow loss 0.047630195 occ loss 0.08110856 time for this batch 0.35210633277893066 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.16018493 flow loss 0.05448007 occ loss 0.10570101 time for this batch 0.3826580047607422 ---------------------------------- train loss for this epoch: 0.140787
time for this epoch 44.75851559638977 No_decrease: 21 ----------------an epoch starts------------------- i_epoch: 172 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15234642 flow loss 0.052120056 occ loss 0.10022261 time for this batch 0.3550863265991211 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.12085326 flow loss 0.045424677 occ loss 0.07542511 time for this batch 0.357712984085083 ---------------------------------- train loss for this epoch: 0.140449
time for this epoch 44.28884172439575 No_decrease: 22 ----------------an epoch starts------------------- i_epoch: 173 # batch: 96 i_batch: 0.0 the loss for this batch: 0.11413476 flow loss 0.044528272 occ loss 0.06960354 time for this batch 0.34873223304748535 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13849099 flow loss 0.051796652 occ loss 0.08669064 time for this batch 0.35308051109313965 ---------------------------------- train loss for this epoch: 0.140208
time for this epoch 43.32447910308838 No_decrease: 23 ----------------an epoch starts------------------- i_epoch: 174 # batch: 96 i_batch: 0.0 the loss for this batch: 0.108738095 flow loss 0.044736966 occ loss 0.063998125 time for this batch 0.3468446731567383 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13380069 flow loss 0.0493363 occ loss 0.08446083 time for this batch 0.3649764060974121 ---------------------------------- train loss for this epoch: 0.140477
time for this epoch 43.95949864387512 No_decrease: 24 ----------------an epoch starts------------------- i_epoch: 175 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15698095 flow loss 0.05489032 occ loss 0.1020868 time for this batch 0.34116244316101074 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.1323297 flow loss 0.050864328 occ loss 0.08146182 time for this batch 0.3483288288116455 ---------------------------------- train loss for this epoch: 0.1404
time for this epoch 43.56815552711487 No_decrease: 25 ----------------an epoch starts------------------- i_epoch: 176 # batch: 96 i_batch: 0.0 the loss for this batch: 0.15370046 flow loss 0.053695735 occ loss 0.10000059 time for this batch 0.2912161350250244 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14084087 flow loss 0.05103545 occ loss 0.08980149 time for this batch 0.41095757484436035 ---------------------------------- train loss for this epoch: 0.139888
time for this epoch 44.165300607681274 No_decrease: 26 ----------------an epoch starts------------------- i_epoch: 177 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14024651 flow loss 0.05307036 occ loss 0.08717229 time for this batch 0.30791807174682617 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.14275609 flow loss 0.048964925 occ loss 0.093787774 time for this batch 0.34859371185302734 ---------------------------------- train loss for this epoch: 0.139742
time for this epoch 42.706565380096436 No_decrease: 27 ----------------an epoch starts------------------- i_epoch: 178 # batch: 96 i_batch: 0.0 the loss for this batch: 0.14240879 flow loss 0.050242215 occ loss 0.09216293 time for this batch 0.3402214050292969 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.13002445 flow loss 0.04637732 occ loss 0.08364364 time for this batch 0.3582756519317627 ---------------------------------- train loss for this epoch: 0.140519
time for this epoch 43.94530916213989 No_decrease: 28 ----------------an epoch starts------------------- i_epoch: 179 # batch: 96 i_batch: 0.0 the loss for this batch: 0.13324368 flow loss 0.048352346 occ loss 0.08488781 time for this batch 0.33770322799682617 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.15433277 flow loss 0.05520725 occ loss 0.099121876 time for this batch 0.396625280380249 ---------------------------------- train loss for this epoch: 0.140337
time for this epoch 43.624903202056885 No_decrease: 29 ----------------an epoch starts------------------- i_epoch: 180 # batch: 96 i_batch: 0.0 the loss for this batch: 0.16184838 flow loss 0.055130765 occ loss 0.10671375 time for this batch 0.3152937889099121 ---------------------------------- i_batch: 64.0 the loss for this batch: 0.12494494 flow loss 0.047416028 occ loss 0.07752556 time for this batch 0.40226030349731445 ---------------------------------- train loss for this epoch: 0.139922
time for this epoch 43.41932439804077 Early stop at the 181-th epoch
def apply_to_vali_test(model, vt, f_o_mean_std):
f = vt["flow"]
f_m = vt["flow_mask"].to(device)
o = vt["occupancy"]
o_m = vt["occupancy_mask"].to(device)
f_mae, f_rmse, o_mae, o_rmse = vali_test(model, f, f_m, o, o_m, f_o_mean_std, hyper["b_s_vt"])
print ("flow_mae", f_mae)
print ("flow_rmse", f_rmse)
print ("occ_mae", o_mae)
print ("occ_rmse", o_rmse)
return f_mae, f_rmse, o_mae, o_rmse
vali_f_mae, vali_f_rmse, vali_o_mae, vali_o_rmse =\
apply_to_vali_test(trained_model, vali, f_o_mean_std)
flow_mae 40.19580209430186 flow_rmse 66.04664759429697 occ_mae 0.033748496773916094 occ_rmse 0.06769058495764328
test_f_mae, test_f_rmse, test_o_mae, test_o_rmse =\
apply_to_vali_test(trained_model, test, f_o_mean_std)
flow_mae 38.93398126549006 flow_rmse 63.92900128825022 occ_mae 0.029798282309958464 occ_rmse 0.06071664007998303